In silico (computed) modelling of doses and dosing regimens associated with morphine levels above international legal driving limits
Boland, Jason W
Johnson, Miriam J
Berry, David J
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Background: Morphine can cause central nervous system side effects which impair driving skills. The legal blood morphine concentration limit for driving is 20 μg/L in France/Poland/Netherlands and 80 μg/L in England/Wales. There is no guidance as to the morphine dose leading to this concentration. Aim: The in silico (computed) relationship of oral morphine dose and plasma concentration was modelled to provide dose estimates for a morphine plasma concentration above 20 and 80 μg/L in different patient groups. Design: A dose–concentration model for different genders, ages and oral morphine formulations, validated against clinical pharmacokinetic data, was generated using Simcyp®, a population-based pharmacokinetic simulator. Setting/participants: Healthy Northern European population parameters were used with age, gender and renal function being varied in the different simulation groups. In total, 36,000 simulated human subjects (100 per modelled group of different ages and gender) received repeated simulated morphine dosing with modified-release or immediate-release formulations. Results: Older age, women, modified-release formulation and worse renal function were associated with higher plasma concentrations. Across all groups, morphine doses below 20 mg/day were unlikely to result in a morphine plasma concentration above 20 μg/L; this was 80 mg/day with the 80 μg/L limit. Conclusion: This novel study provides predictions of the in silico (computed) dose–concentration relationship for international application. Individualised morphine prescribing decisions by clinicians must be informed by clinical judgement considering the individual patient’s level of impairment and insight irrespective of the blood morphine concentration as people who have impaired driving will be breaking the law. Taking into account expected morphine concentrations enables improved individualised decision making.
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